{"type":"ScholarlyArticle","title":"LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work","authors":[{"name":"Giuseppe De Giacomo","slug":"giuseppe-de-giacomo","given":"Giuseppe","family":"De Giacomo","affiliation":"Sapienza University of Rome"},{"name":"Marco Favorito","slug":"marco-favorito","given":"Marco","family":"Favorito","affiliation":"Sapienza University of Rome; Banca d 'Italia"},{"name":"Jianwen Li","slug":"jianwen-li","given":"Jianwen","family":"Li","affiliation":"East China Normal University"},{"name":"Moshe Y. Vardi","slug":"moshe-y-vardi","given":"Moshe Y.","family":"Vardi","affiliation":"Rice University"},{"name":"Shengping Xiao","slug":"shengping-xiao","given":"Shengping","family":"Xiao","affiliation":"East China Normal University"},{"name":"Shufang Zhu","slug":"shufang-zhu","given":"Shufang","family":"Zhu","affiliation":"Sapienza University of Rome"}],"abstract":"Synthesis techniques for temporal logic specifications are typically based on exploiting symbolic techniques, as done in model checking. These symbolic techniques typically use backward fixpoint computation. Planning, which can be seen as a specific form of synthesis, is a witness of the success of forward search approaches. In this paper, we develop a forward-search approach to full-fledged Linear Temporal Logic on finite traces (LTLf) synthesis. We show how to compute the Deterministic Finite Automaton (DFA) of an LTLf formula on-the-fly, while performing an adversarial forward search towards the final states, by considering the DFA as a sort of AND-OR graph. Our approach is characterized by branching on suitable propositional formulas, instead of individual evaluations, hence radically reducing the branching factor of the search space. Specifically, we take advantage of techniques developed for knowledge compilation, such as Sentential Decision Diagrams (SDDs), to implement the approach efficiently.","doi":"10.24963/ijcai.2022/359","url":"https://ijcai.org/proceedings/2022/359","pdf":"https://ijcai.org/proceedings/2022/0359.pdf","markdown":"https://ijcai.org/proceedings/2022/359.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/359","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":359,"pages":{"first":2591,"last":2598},"keywords":["Planning and Scheduling: Theoretical Foundations of Planning","Knowledge Representation and Reasoning: Reasoning about actions","Agent-based and Multi-agent Systems: Formal Verification, Validation and Synthesis"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986638","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984781","length_sec":335,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bacchus and Kabanza, 1998] F. 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